Ecological Complexity最新文献

Eco-epidemiology integrates ecological and epidemiological approaches to analyze both the impact of infectious diseases on ecological communities and how interspecific interactions can alter disease dynamics. With the aim of extracting general principles of eco-epidemiological dynamics, this article presents a review of the literature focusing on predator–prey type ordinary differential equation models with disease in one of the species. We included 81 articles that were categorized according to prey growth function, disease transmission function, epidemiological model compartments, and predator functional response. The findings reveal that these models share a common mathematical lineage, which in turn facilitates the construction of models based on the general assumptions identified in this study. The most prevalent models tend to assume logistic prey growth, a bilinear incidence rate for disease transmission, an epidemiological model of the Susceptible–Infected type, and a Holling Type II predator functional response.

Complexity has radically changed human understanding of the world environment and continues challenging our best scientific theories. In a rapidly changing research landscape, historical and philosophical insights into Complexity can heighten awareness of the proper theoretical perspectives scientists should adopt to advance the study of biocomplexity, including ecological complexity. The present work aims to deepen this awareness and disclose how researchers should generally approach, scientifically and philosophically, the question of what Complexity is, which is of great importance not only to the scientific community but also far beyond. First, this article reviews some critical historical turning points that led to Complexity. Second, the paper discusses philosophical-scientific approaches to the emergence as one of the most critical features of complex systems. The critical ideas behind attempts to understand the generators of complexity in nature are then presented, focusing on the living world. Finally, the review focuses on understanding the ecosystem- and organism-oriented perspectives of biocomplexity. We conclude that the genuine problem of the origin of complexity theory and biocomplexity will continue to inspire generations of researchers to search for new, more comprehensive mathematical and computational frameworks to explain biological hierarchies in order to further advance the scientific understanding of life.

Historically, the idea that ecosystems possess geographical boundaries has been dismissed as both naïve and impractical. But advancements in remote sensing have led to the reliable detection of spatial regimes that seem to provide early warning of a potential critical transition. This invites a reexamination of the role geographical boundaries play in explanations of the resilience concept. Despite apparent ontological imprecision, defining the boundaries of an ecosystem geographically, instead of dynamically (i.e., as collections of feedback mechanisms), dilates explanations of resilience to improve understanding of the history of contingent causal dynamics that culminate in emergent self-organization at a single scale. To demonstrate the utility of geographical boundaries, three related discussions connect spatial resilience theory with elements of island biogeography theory: (1) the function of stepping-stones as ecological filters, (2) mobile links as examples of the rescue effect, and (3) the way assembly rules and successive equilibria map onto the forward loop of the adaptive cycle heuristic.

Understanding dynamical behavior of a spatially distributed population is crucial to conservation and management of endangered species. This paper considers predator–prey systems with Beddington–DeAngelis functional response, where the predator moves between source–sink patches asymmetrically and acts as an agent. Our aim is to show how agent-based diffusion affects dynamics of the system and total population abundance of the species. Using dynamical systems theory, we demonstrate stability of positive equilibria in the system, which implies coexistence of the species and change of abundance by diffusion. Moreover, we show Hopf and Bautin bifurcations with multiple limit cycles, which implies multiple oscillations of populations and even extinction of species. Furthermore, this work demonstrates that diffusion in the system may lead to results reversing those without diffusion. The diffusion could change dynamics of the system between coexistence at a steady state and persistence in periodic oscillation, while evolution in asymmetry of diffusion could make the predator reach a total abundance larger than that without diffusion, even reach the maximal abundance. Our results are consistent with experimental observations and are important in studying conservation of biodiversity.

Incorporating temporal variation in models is one of the most important challenges in food web research. One of the environments where time causes profound changes is the intertidal zone, where the immersion-emersion cycle drastically changes the abiotic and biotic conditions. Intertidal rocky shores have been intensively studied, however the variation in the complex food web network that occurs during a tidal cycle remains undescribed. Highly resolved food web networks were assembled for an intertidal reef depicting the food web during low and high tide, and with and without tide pools. It was concluded that high tide adds new species to the web, but it does not add complexity since network connectance was not changed. This occurs because incoming species are mostly highly generalist fish, which add many new links to the web. Tide pools, however, add not only diversity but also complexity. Webs were dominated by intermediate species, with the proportion of top consumers fluctuating throughout the tidal cycle, being lowest during low tide and highest at high tide, due to the incoming larger vertebrate predators. Consumer taxa outnumbered resource taxa, except at low tide when pools are present. Mean trophic level was lowest at low tide (2.3) and highest at high tide with pools (2.6). Omnivory was high and showed little change. “Chain”, the number of links connecting top to basal species, was stable but low. This implies that disturbance can rapidly travel bottom-up or top-down through predator-prey links. The increased connectance given by the addition of tide pools likely increases robustness to disturbances, an important feature in coastal areas so often impacted by human action.

Dynamical systems modeling (DSM) explores how a system evolves in time when its elements and the relationships between them are known. The basic idea is that the structure of a dynamical system, expressed by coupled differential or difference equations, determines attractors of the system and, in turn, its behavior. This leads to structural understanding that can provide insights into qualitative properties of real systems, including ecological and social-ecological systems (SES). DSM generally does not aim to make specific quantitative predictions or explain singular events, but to investigate consequences of different assumptions about a system's structure. SES dynamics and possible causal relationships in SES get revealed through manipulation of individual interactions and observation of their consequences. Structural understanding is therefore particularly valuable for assessing and anticipating the consequences of interventions or shocks and managing transformation toward sustainability. Taking into account social and ecological dynamics, recognizing that SES may operate on different time scales simultaneously and that achieving an attractor might not be possible or relevant, opens up possibilities for DSM setup and analysis. This also highlights the importance of assumptions and research questions for model results and calls for closer connection between modeling and empirics. Understanding the potential and limitations of DSM in SES research is important because the well-developed and established framework of DSM provides a common language and helps break down barriers to shared understanding and dialog within multidisciplinary teams. In this primer we introduce the basic concepts, methods, and possible insights from DSM. Our target audience are both beginners in DSM and modelers who use other model types, both in ecology and SES research.

In this paper, in view of the senescence of plant and the decay of wrack, time delays are introduced into the plant-wrack model. The effects of wrack decay and time delay on the dynamical behaviors of the diffusive plant-wrack model are studied analytically and numerically. When the delay is zero, the wrack decay will induce the change of stability of the unique equilibrium point, further lead to the occurrence of the Hopf bifurcation and the Turing instability. When the delay is present, the conditions for the occurrence of the Hopf bifurcation are established. By comparing the results of the model without and with delay, it is found that the increases of delay may induce no stability switches, a single stability switch or multiple stability switches, when the value of wrack decay can stabilize model with zero delay. When the value of wrack decay can destabilize model with zero delay, numerical simulations show that the small delay may cause homogeneous distributions of vegetation, while the larger delay may cause the emergence of periodic oscillation of vegetation. The obtained results provide a basis for understanding the spatiotemporal evolution of such a plant-wrack model with delay.

In Earth surface systems (ESS), everything is connected to everything else, an aphorism often called the First Law of Ecology and of geography. Such linkages are not always direct and unmediated, but many ESS, represented as networks of interacting components, attain or approach full, direct connectivity among components. The question is how and why this happens at the system or network scale. The crowded landscape concept dictates that linkages and connections among ESS components are inevitable. The connection selection concept holds that the linkages among components are (often) advantageous to the network and are selected for, and thereby preserved and enhanced. These network advantages are illustrated via algebraic graph theory. For a given number of components in an ESS, as the number of links or connections increases, spectral radius, graph energy, and algebraic connectivity increase. While the advantages (if any) of increased complexity are unclear, higher spectral radii are directly correlated with higher graph energy. The greater graph energy is associated with more intense feedback in the system, and tighter coupling among components. This in turn reflects advantageous properties of more intense cycling of water, nutrients, and minerals, as well as multiple potential degrees of freedom for individual components to respond to changes. The increase of algebraic connectivity reflects a greater ability or tendency for the network to respond to changes in concert.

In this paper, we study the vulnerability of forest ecosystems perturbed by extreme events, such as those arising from climate change. To investigate the complex interactions between the biological dynamics of the forest and the climatic activity, we construct an original hybrid model, obtained by coupling a continuous reaction–diffusion system, which describes the spatio-temporal dynamics of the forest ecosystem, with a discrete probabilistic process, which models the possible occurrences of extreme events. Properties of ecological interest are considered: invariance of the persistence equilibrium, attraction to the extinction equilibrium and emergence of degraded states. Those properties of the hybrid model are verified through an extension of the Statistical Model Checking framework. We establish the existence of a threshold above which the persistence equilibrium of the forest ecosystem is compromised and give a numerical assessment of this threshold in terms of the probability and intensity of extreme events. We also present non-trivial parameter conditions for which the forest ecosystem converges to a degraded savanna-like state.